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Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics

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  • H Couclelis

    (Department of Geography, University of California, Santa Barbara, CA 93106, USA)

Abstract

Cellular spaces have recently received a lot of attention in computer science and elsewhere as models capable of bridging the gap between disaggregate and aggregate description. Despite their obvious spatial interpretation, standard cell-space models are too constrained by their background conventions to be useful in realistic geographic applications. In this paper, a generalization of the cell-space principle is presented, based on discrete model theory, and then applied to a hypothetical but fairly complex problem of individual decision and large-scale urban change. The paper ends with a discussion of the wider import of this methodology, which has close links with, among other things, bifurcation theory, cognitive science modeling of individual decision and behavior, and other issues of actual or potential interest to geographers.

Suggested Citation

  • H Couclelis, 1985. "Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics," Environment and Planning A, , vol. 17(5), pages 585-596, May.
    • Handle: RePEc:sae:envira:v:17:y:1985:i:5:p:585-596
    DOI: 10.1068/a170585
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    Cited by:

    1. Patterson, Zachary & Kryvobokov, Marko & Marchal, Fabrice & Bierlaire, Michel, 2010. "Disaggregate models with aggregate data: Two UrbanSim applications," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 5-37.
    2. Basse, Reine Maria, 2013. "A constrained cellular automata model to simulate the potential effects of high-speed train stations on land-use dynamics in trans-border regions," Journal of Transport Geography, Elsevier, vol. 32(C), pages 23-37.
    3. Liang, Lu & Li, Xuecao & Huang, Yanbo & Qin, Yuchu & Huang, Huabing, 2017. "Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance," Ecological Modelling, Elsevier, vol. 354(C), pages 1-10.
    4. Liu, Xiaoping & Li, Xia & Shi, Xun & Wu, Shaokun & Liu, Tao, 2008. "Simulating complex urban development using kernel-based non-linear cellular automata," Ecological Modelling, Elsevier, vol. 211(1), pages 169-181.
    5. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    6. Bosch, Martí & Chenal, Jérôme & Joost, Stéphane, 2019. "Addressing urban sprawl from the complexity sciences," MPRA Paper 93489, University Library of Munich, Germany.
    7. A’kif AL-FUGARA & Abdel Rahman AL-SHABEEB & Yahya AL-SHAWABKEH & Hani AL-AMOUSH & Rida AL-ADAMAT, 2018. "Simulation And Prediction Of Urban Spatial Expansion In Highly Vibrant Cities Using The Sleuth Model: A Case Study Of Amman Metropolitan, Jordan," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(1), pages 37-56, February.
    8. Federico Amato & Biagio Antonio Maimone & Federico Martellozzo & Gabriele Nolè & Beniamino Murgante, 2016. "The Effects of Urban Policies on the Development of Urban Areas," Sustainability, MDPI, vol. 8(4), pages 1-22, March.

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